Browsing by Author "Wang, Sheng-De"

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Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
(2018-01-01)
Liu, Yen-Cheng
;
Yeh, Yu-Ying
;
Fu, Tzu-Chien
;
Wang, Sheng-De
;
Chiu, Wei-Chen
While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.
DOMAIN-ADAPTIVE GENERATIVE ADVERSARIAL NETWORKS FOR SKETCH-TO-PHOTO INVERSION
(2017-01-01)
Liu, Yen-Cheng
;
Chiu, Wei-Chen
;
Wang, Sheng-De
;
Wang, Yu-Chiang Frank
;
交大名義發表
Generating photo-realistic images from multiple style sketches is one of challenging tasks in image synthesis with important applications such as facial composite for suspects. While machine learning techniques have been applied for solving this problem, the requirement of collecting sketch and face photo image pairs would limit the use of the learned model for rendering sketches of different styles. In this paper, we propose a novel deep learning model of Domain-adaptive Generative Adversarial Networks (DA-GAN). The design of DA-GAN performs cross-style sketch-to-photo inversion, which mitigates the difference across input sketch styles without the need to collect a large number of sketch and face image pairs for training purposes. In experiments, we show that our method is able to produce satisfactory results as well as performing favorably against state-of-the-art approaches.